A Heterogeneous Graph Neural Model for Cold-start Recommendation

Siwei Liu, I. Ounis, C. Macdonald, Zaiqiao Meng
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引用次数: 70

Abstract

The users' historical interactions usually contain their interests and purchase habits based on which personalised recommendations can be made. However, such user interactions are often sparse, leading to the well-known cold-start problem when a user has no or very few interactions. In this paper, we propose a new recommendation model, named Heterogeneous Graph Neural Recommender (HGNR), to tackle the cold-start problem while ensuring effective recommendations for all users. Our HGNR model learns users and items' embeddings by using the Graph Convolutional Network based on a heterogeneous graph, which is constructed from user-item interactions, social links and semantic links predicted from the social network and textual reviews. Our extensive empirical experiments on three public datasets demonstrate that HGNR significantly outperforms competitive baselines in terms of the Normalised Discounted Cumulative Gain and Hit Ratio measures.
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冷启动推荐的异构图神经模型
用户的历史互动通常包含他们的兴趣和购买习惯,可以根据这些兴趣和习惯做出个性化的推荐。然而,这样的用户交互通常是稀疏的,当用户没有或很少交互时,就会导致众所周知的冷启动问题。在本文中,我们提出了一种新的推荐模型,称为异构图神经推荐(HGNR),以解决冷启动问题,同时确保对所有用户的有效推荐。我们的HGNR模型通过使用基于异构图的图卷积网络来学习用户和项目的嵌入,该异构图由用户-项目交互、社交网络预测的社交链接和语义链接以及文本评论构建而成。我们在三个公共数据集上进行了广泛的实证实验,结果表明,在标准化贴现累积增益和命中率度量方面,HGNR显著优于竞争基准。
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